Overview
- Maximizes reader insights into optimal energy distribution strategies for the microgrid in a smart grid environment
- Reviews and analyzes the key recent research and trends in microgrid systems and models
- Provides real-world examples of smart grids and the microgrid problem from real life case studies as well as looking towards developing trends
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
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Table of contents (4 chapters)
Keywords
About this book
Presenting an optimal energy distribution strategy for microgrids in a smart grid environment, and featuring a detailed analysis of the mathematical techniques of convex optimization and online algorithms, this book provides readers with essential content on how to achieve multi-objective optimization that takes into consideration power subscribers, energy providers and grid smoothing in microgrids. Featuring detailed theoretical proofs and simulation results that demonstrate and evaluate the correctness and effectiveness of the algorithm, this text explains step-by-step how the problem can be reformulated and solved, and how to achieve the distributed online algorithm on the basis of a centralized offline algorithm. Special attention is paid to how to apply this algorithm in practical cases and the possible future trends of the microgrid and smart grid research and applications. Offering a valuable guide to help researchers and students better understand the new smart grid, this book will also familiarize readers with the concept of the microgrid and its relationship with renewable energy.
Authors and Affiliations
About the authors
SHIWEN MAO received the Ph.D. degree in electrical and computer engineering from Polytechnic University, Brooklyn, NY, USA. Currently, he is the McWane Associate Professor with the Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA. His current research interests include cross-layer optimization of wireless networks and multimedia communications, with current focus on cognitive radios, femtocells, 60 GHz mmWave networks, free space optical networks, and smart grid. He is on the Editorial Board of the IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS and the IEEE Communications Surveys and Tutorials. He received the 2013 IEEE ComSoc MMTC Outstanding Leadership Award and the NSF CAREER Award in 2010. He is a co-recipient of the IEEE ICC 2013 Best Paper Award and the 2004 IEEE Communications Society Leonard G. Abraham Prize in the Field of Communications Systems.
R. M. NELMS received the B.E.E. and M.S. degrees in electrical engineering from Auburn University, AL, USA, in 1980 and 1982, respectively. He received the Ph.D. degree in electrical engineering from Virginia Polytechnic Institute and State University, Blacksburg, VA, USA, in 1987. He is currently a Professor and Chair with the Department of Electrical and Computer Engineering, Auburn University. His current research interests include power electronics, power systems, and electric machinery. In 2004, he was named an IEEE Fellow "for technical leadership and contributions to applied power electronics." He is a Registered Professional Engineer in Alabama.
Bibliographic Information
Book Title: Online Algorithms for Optimal Energy Distribution in Microgrids
Authors: Yu Wang, Shiwen Mao, R. Mark Nelms
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-3-319-17133-3
Publisher: Springer Cham
eBook Packages: Energy, Energy (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2015
Softcover ISBN: 978-3-319-17132-6Published: 12 June 2015
eBook ISBN: 978-3-319-17133-3Published: 30 May 2015
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: XIII, 91
Number of Illustrations: 3 b/w illustrations, 21 illustrations in colour
Topics: Energy Efficiency, Power Electronics, Electrical Machines and Networks, Innovation/Technology Management